Enhancement of radar micro-doppler classification performance in noisy conditions

Micro-Doppler radar signals for different target types or different movements of a target are also different from each other. This leads to usage of micro-Doppler radar signals as a source of classifying targets. Many micro-Doppler based target classification studies have been published in the last ten years. The studies which are done using experimental radar data are usually carried out in situations where target is close to the radar and clutter effect is minimal. In simulation studies, noise and clutter effects are also ignored. Measurement of classification performance for idealistic conditions in which noise and clutter effects are not taken into account makes the outcomes of the study doubtful. In this thesis, a classification study of micro-Doppler based target signatures is carried out using simulation data. While generating radar simulation data of targets, noise and clutter effects are also modeled. Classification performance obtained from the data containing noise and clutter is compared to noise free data. In addition, a method is proposed to increase the performance in noisy situations using image processing techniques. The results are obtained with two different classifiers and the proposed method has made an increase from 5% to 15% in the classification performance.